MRI is a powerful medical imaging modality that boasts superior soft tissue contrast which can be leveraged to diagnose a variety of adverse health conditions. Compared to other medical imaging methods, however, MRI is relatively slow. Many previous works have developed methods to decrease the data requirements for solving the ill-posed inverse problem of sub-sampled image reconstruction [1]}, [2]}, including through deep learning [3]}, [4]}, [5]}, [6]}, [7]}. Even with sub-sampling, MRI is highly susceptible to patient motion [8]}, which leads to inconsistencies in measurement data at the time of reconstruction. The image degradation due to motion-corrupt data can manifest as blurring or ghosting which possibly render the image nondiagnostic, and often necessitates reacquiring the scan. It has been reported that on average motion corrupted scans in a pediatric setting have led some hospitals to incur nearly \(\$365K\)  in additional costs each year due to motion artifacts [9]}.
